Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/8549
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dc.contributor.authorAzzopardi, George-
dc.contributor.authorRodriguez-Sanchez, Antonio-
dc.contributor.authorPiater, Justus-
dc.contributor.authorPetkov, Nicolai-
dc.date.accessioned2016-02-29T10:21:20Z-
dc.date.available2016-02-29T10:21:20Z-
dc.date.issued2014-
dc.identifier.citationPLOS ONE. 2014, Vol.9(7)en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar//handle/123456789/8549-
dc.description.abstractWe propose a computational model of a simple cell with push-pull inhibition, a property that is observed in many real simple cells. It is based on an existing model called Combination of Receptive Fields or CORF for brevity. A CORF model uses as afferent inputs the responses of model LGN cells with appropriately aligned center-surround receptive fields, and combines their output with a weighted geometric mean. The output of the proposed model simple cell with push-pull inhibition, which we call push-pull CORF, is computed as the response of a CORF model cell that is selective for a stimulus with preferred orientation and preferred contrast minus a fraction of the response of a CORF model cell that responds to the same stimulus but of opposite contrast. We demonstrate that the proposed push-pull CORF model improves signal-to-noise ratio (SNR) and achieves further properties that are observed in real simple cells, namely separability of spatial frequency and orientation as well as contrast-dependent changes in spatial frequency tuning. We also demonstrate the effectiveness of the proposed push-pull CORF model in contour detection, which is believed to be the primary biological role of simple cells. We use the RuG (40 images) and Berkeley (500 images) benchmark data sets of images with natural scenes and show that the proposed model outperforms, with very high statistical significance, the basic CORF model without inhibition, Gabor-based models with isotropic surround inhibition, and the Canny edge detector. The push-pull CORF model that we propose is a contribution to a better understanding of how visual information is processed in the brain as it provides the ability to reproduce a wider range of properties exhibited by real simple cells. As a result of push-pull inhibition a CORF model exhibits an improved SNR, which is the reason for a more effective contour detection.en_GB
dc.language.isoenen_GB
dc.publisherPLOSen_GB
dc.rightsinfo:eu-repo/semantics/openAccessen_GB
dc.subjectInhibitionen_GB
dc.subjectNeuronsen_GB
dc.subjectComputational neuroscienceen_GB
dc.subjectComputer visionen_GB
dc.subjectNonparametric signal detectionen_GB
dc.titleA push-pull CORF model of a simple cell with antiphase inhibition improves SNR and contour detectionen_GB
dc.typearticleen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.description.reviewedpeer-revieweden_GB
dc.identifier.doi10.1371/journal.pone.0098424-
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